利用matlab，怎么写出一个完整的自适应的遗传算法优化BP神经网络的代码

以下是一个基本的自适应遗传算法优化BP神经网络的MATLAB代码：

% 读取数据
load iris_dataset.mat
X = irisInputs';
Y = irisTargets';

% 遗传算法参数
pop_size = 50; % 种群大小
num_var = 4*6+3; % 变量数量，即神经网络的权重和偏置
num_gen = 100; % 迭代次数
mutation_rate = 0.05; % 变异率
selection_rate = 0.5; % 选择率
tournament_size = 3; % 锦标赛规模

% 神经网络参数
hidden_layers = [6]; % 隐藏层节点数
learning_rate = 0.1; % 学习率
momentum = 0.9; % 动量因子

% 初始化种群
pop = rand(pop_size, num_var) * 2 - 1;

for gen = 1:num_gen
    % 计算每个个体的适应度
    fitness = zeros(pop_size, 1);
    for i = 1:pop_size
        weights = reshape(pop(i,:), [4,6,1,6,3]);
        net = feedforwardnet(hidden_layers);
        net = configure(net, X, Y);
        net = setwb(net, weights);
        net.trainParam.lr = learning_rate;
        net.trainParam.mc = momentum;
        net.trainParam.epochs = 100;
        net = train(net, X, Y);
        fitness(i) = 1 - perform(net, Y, net(X));
    end

    % 选择操作
    [~, idx] = sort(fitness, 'descend');
    elite = pop(idx(1:ceil(selection_rate*pop_size)), :);
    rest = pop(idx(ceil(selection_rate*pop_size)+1:end), :);
    
    % 交叉操作
    offspring = zeros(size(rest));
    for i = 1:size(offspring, 1)
        parent1 = rest(randi(size(rest, 1)), :);
        parent2 = rest(randi(size(rest, 1)), :);
        point = randi(size(parent1, 2));
        offspring(i,:) = [parent1(1:point) parent2(point+1:end)];
    end
    
    % 变异操作
    mutation = (rand(size(rest)) < mutation_rate);
    offspring(mutation) = offspring(mutation) + randn(sum(sum(mutation)), 1);
    
    % 合并种群
    pop = [elite; offspring];
end

% 找到最优个体并计算适应度
fitness = zeros(pop_size, 1);
for i = 1:pop_size
    weights = reshape(pop(i,:), [4,6,1,6,3]);
    net = feedforwardnet(hidden_layers);
    net = configure(net, X, Y);
    net = setwb(net, weights);
    net.trainParam.lr = learning_rate;
    net.trainParam.mc = momentum;
    net.trainParam.epochs = 100;
    net = train(net, X, Y);
    fitness(i) = 1 - perform(net, Y, net(X));
end
[best_fitness, best_idx] = max(fitness);
best_weights = reshape(pop(best_idx,:), [4,6,1,6,3]);

% 用最优个体构建神经网络并测试
net = feedforwardnet(hidden_layers);
net = configure(net, X, Y);
net = setwb(net, best_weights);
net.trainParam.lr = learning_rate;
net.trainParam.mc = momentum;
net.trainParam.epochs = 100;
net = train(net, X, Y);
outputs = net(X);
errors = gsubtract(Y,outputs);
performance = perform(net,Y,outputs);

上述代码中，我们使用了MATLAB的`feedforwardnet`函数来构建BP神经网络，并使用遗传算法来优化网络的权重和偏置。遗传算法的选择、交叉和变异操作的具体实现可以根据需要进行修改。